Hi Guys,

Apologies if this isn’t written correctly - my first post, go easy on me ;-)… I’m not a statistician, but when I heard about prob. programming I thought it was very useful tool for financial valuation. So, here goes my question.

The value of a company can be approximated with a number of models. In this case, I am going to use a return on invested capital (roic) valuation model, which is defined as:

`enterprise_value = ((ebit*(1-tax))*(1-(g/roic)))/(ke - g)`

Excuse my lack of knowledge, please correct me if I am wrong here… enterprise_value is clearly a determinaistic variable which is based on known values for EBIT and TAX, but g and ke are probabilistic variables which have a uniform distribution.

The issue I have is that I do not know what distribution enterprise_value should have, and so I am wondering how to generate values for enterprise_value using the model…?

So far i have the following:

```
import pymc3 as pm
basic_model = pm.Model()
tax = 0.30
ebit = 1
with basic_model:
#priors
ke = pm.Uniform('ke', 0.03, 0.05)
g = pm.Uniform('g', 0.05, 0.20)
roic = pm.Uniform('roic', 0.10, 0.20)
#expected value
ev = ((ebit*(1-tax))*(1-(g/roic)))/(ke - g)
#likelihood - im guessing the likelihood is the forecast sample...?
y_obs = pm.Uniform('y_obs', observed=ev)
```

Ive taken a stab at it withe the likelihood being uniform, but this is where my lack of stats knowledge is causing me some problems - as I’m just guessing that is the right thing to do here… As stated before, I dont know what the distribution will look like for y_obs, so can pymc3 do that?

Any help would be appreciated!